Point cloud from low-altitude aerial imagery from unmanned aerial system (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (LAZ file)

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Frequently anticipated questions:

What does this data set describe?

Title:
Point cloud from low-altitude aerial imagery from unmanned aerial system (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (LAZ file)
Abstract:
This point cloud was derived from low-altitude aerial images collected from an unmanned aerial system (UAS) flown in the Cape Cod National Seashore on 1 March, 2016. The objective of the project was to evaluate the quality and cost of mapping from UAS images. The point cloud contains 434,096,824 unclassifed and unedited geolocated points. The points have horizontal coordinates in NAD83(2011) UTM Zone 19 North meters, vertical coordinates in NAVD88 meters, and colors in the red-green-blue (RGB) schema. The points were generated in photogrammetric software (Agisoft Photoscan Professional v. 1.2.6) from 1122 digital images taken approximately 120 m above the ground with a Canon Powershot SX280 12-mexapixel digital camera mounted in a Skywalker X8 flying wing operated by Raptor Maps, Inc., contractors to the U.S. Geological Survey. The photogrammetric processing incorporated 30 ground control points. The entire, un-edited unclassified point cloud is provided in standard LAZ format. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  1. How might this data set be cited?
    U.S. Geological Survey, 2017, Point cloud from low-altitude aerial imagery from unmanned aerial system (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (LAZ file): data release DOI:10.5066/F75M63WJ, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Sherwood, C.R., 2017, Point cloud from low-altitude aerial imagery from unmanned aerial system (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (LAZ file): U.S. Geological Survey data release, https://doi.org/10.5066/F75M63WJ.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -69.9576
    East_Bounding_Coordinate: -69.9392
    North_Bounding_Coordinate: 41.8503
    South_Bounding_Coordinate: 41.8204
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/58924177e4b072a7ac145769?name=CACO_2016-03-01_point_cloud_browse_image.jpg (JPEG)
    Image of a portion of the point cloud generated from photogrammetry and low-altitude aerial images obtained with unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts, 1 March 2016. This is a cropped image that shows a portion of Nauset Marsh.
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 01-Mar-2016
    Currentness_Reference:
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: LAZ binary data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Point data set. It contains the following vector data types (SDTS terminology):
      • Point (434,096,824)
    2. What coordinate system is used to represent geographic features?
      The map projection used is Universal Transverse Mercator.
      Projection parameters:
      Scale_Factor_at_Central_Meridian: 0.999600
      Longitude_of_Central_Meridian: -69.00000
      Latitude_of_Projection_Origin: 0.000
      False_Easting: 500000.0000
      False_Northing: 0.0000
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 0.001
      Ordinates (y-coordinates) are specified to the nearest 0.001
      Planar coordinates are specified in meters
      The horizontal datum used is North American Datum of 1983.
      The ellipsoid used is Geodetic Reference System 80.
      The semi-major axis of the ellipsoid used is 6378137.000000.
      The flattening of the ellipsoid used is 1/298.257224.
      Vertical_Coordinate_System_Definition:
      Altitude_System_Definition:
      Altitude_Datum_Name: North American Vertical Datum of 1988 (NAVD88)
      Altitude_Resolution: 0.001
      Altitude_Distance_Units: meters
      Altitude_Encoding_Method:
      Explicit elevation coordinate included with horizontal coordinates
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    The attribute information associated with points in the LAZ file is standard, as described in ASPRS (2013). Attributes include location (northing, easting, and elevation in UTM Zone 19 North meters in the NAD83(2011) and NAVD88 coordinate systems), color (red, blue, and green components), intensity, and classification. All points are classifed as 0 (unclassifed). The LAZ file format is described in Isenburg (2013).
    Entity_and_Attribute_Detail_Citation:
    See cross-references for complete citations of ASPRS (2013) and Isenburg (2013).

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • U.S. Geological Survey
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Christopher R. Sherwood
    Research Oceanographer
    384 Woods Hole Road
    Woods Hole, Massachusetts

    508-548-8700 x2269 (voice)
    508-457-2310 (FAX)
    csherwood@usgs.gov

Why was the data set created?

The purpose of generating and releasing this point cloud was to provide a basis for additional products (digital elevation maps and orthophoto mosaics) and for use in researching methods of automatic point-cloud classification. The broader goal of the project was to evaluate the quality of images and photogrammetric products and evaluate the cost and feasibility of mapping from UAS images.

How was the data set created?

  1. From what previous works were the data drawn?
    raw aerial digital images and text files with ground control point (GCPs) (source 1 of 1)
    Sherwood, Christopher R., 2016, Low-altitude aerial imagery and related field observations associated with unmanned aerial system (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016: ScienceBase Catalog, USGS Data Release Products Low-altitude aerial imagery and related field observations associated with unmanned aerial system (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016, U. S. Geological Survey, Reston, VA.

    Online Links:

    Type_of_Source_Media: raster digital images and text data files
    Source_Contribution:
    The digital images are the raw data used to produce subsequent photogrammetric products. The ground control points (GCPs) are used to geolocate the photogrammetric products.
  2. How were the data generated, processed, and modified?
    Date: 19-Sep-2016 (process 1 of 3)
    Processing Images to Build a Dense Point Cloud
    The following process was used to generate the dense point cloud. The images used were acquired from the camera designated rgb2 during flight 1 over Coast Guard Beach, Nauset Inlet, and Nauset Marsh on 1 March 2016 (Sherwood, 2016). Thirty ground control points (GCPs) were incorporated in the photogrammetric processing. Details and locations of the images and GCPs were provided by Sherwood (2016). The processing was performed using Agisoft Photoscan Professional v. 1.2.6 build 2834 (64 bit) software. The computer was a HP Z800 workstation running Windows 7 Enterprise SP1 operating system with dual 6-core Xeon X5675 CPUs running at 3.06 GHz with 96 GB RAM.
    Initial alignment
    1) Using the “Add photos…” tool, all 1246 of the photos in the directory flight_1_rgb_2 (Sherwood, 2016) were added to a single chunk.
    2) Using ”Convert”, the coordinate system of the images (called “cameras” in Photoscan) was converted from native GPS geographic units (latitude/longitude, assumed to be in the WGS84 coordinate system) to meters in NAD83/UTM zone 19N (EPSG::26919). Camera location accuracy was left at the default 10 m (found in Reference Settings on the Reference Pane).
    3) ”Align Photos” was selected to align all of the cameras using the following settings: Accuracy: “Medium” (which downsampled the images to 1/4 by using pixels from every other row and column); Pair selection: “Reference” (which used GPS information identify nearby images when searching for tie points); Key point limit: 60,000; Tie point limit; 0 (unlimited). Adaptive camera model fitting option was selected. 1127 cameras (images with varying viewpoints) were initially aligned.
    4) ”Optimize Cameras” was used to perform initial lens calibration and camera alignment. Lens-calibration parameters f, cx, cy, k1, k2, k3, b1, b2, p1, and p2 were included; higher-order parameters k4, p3, and p4 were not. These parameters define focal length (f), pixel coordinates of the principal point (cx, cy), and radial distortion coefficients (k1, k2, k3, k4, p1, p2, p3, and p4). The software generates a metric for assessing model fit called the standard unit weight error (SUWE). Values close to 1.0 are optimal. The initial SUWE was 0.165 and the overall alignment error for the cameras was 35.26 m.
    5) ”Optimize Cameras” was performed again, this time including parameter k4, but there was no change in the SUWE.
    6) Image quality for the photos was estimated using “Estimate image quality…”. The resulting image quality metrics (which are relative non-dimensional measures) ranged from 1.52 to 0, with only 9 images below 0.5. Five images had 0 quality (these were all images of sand sheets), but all were aligned.
    Ground control points
    1) The bounding box was manually adjusted to delineate the region for further processing. The northern, southern, and western edges were based on the extent of complete photo coverage, and the eastern (seaward) boundary was placed just offshore of visible land features. The vertical extent of the bounding box was reduced to a few meters above and below the topography evident in the sparse point cloud.
    2) “Detect Markers” was used to automatically identify targets in the photos, with settings “Cross (non-coded)” and a tolerance of “100” (on a scale of zero to 100, with 100 being the least discriminating). A total of 54 possible markers were automatically detected, but most were manually identified in the photos as false positives and removed. All 15 of the 4-ft square black and white targets deployed were automatically detected, but the other targets (black plastic trash bags and in-place features; see Sherwood, 2016) were not. The automatically-generated marker labels were manually changed to match the names in the GCP location file ("CACO_ground_control_points_20160301.txt" file in Sherwood, 2016) with reference to a map of the labeled GCPs.
    3) “Import markers” was used to load the GCP location file ("CACO_ground_control_points_20160301.txt" file in Sherwood, 2016), which assigned coordinates (northing, easting, and elevation in UTM Zone 19 North meters in NAD83 and NAVD88 coordinate systems) from the location file to the detected markers, and placed new markers for the GCPs that had not been auto-detected.
    4) The locations of all markers were established in all of the images in which they appeared, except when the image of the target was so poor that the reference point on the target could not be precisely determined. This was a manual process aided by the ability of the software to identify images in which each marker appeared and to maintain a centered view at constant zoom level across all of those images. Each of those images was inspected to verify and adjust the precise marker placement. Manual placement was a painstaking and somewhat subjective process that introduced slight uncertainties into the GCP location in the images. However, our experience indicates that addition of GCPs and pinpointing targets in as many images as possible improves the final alignment of the point cloud.
    5) Two “a posteriori” GCPs were added in the marsh, as discussed in the process steps for the GCPs in Sherwood (2016). These were points placed on features visible in several images. Horizontal coordinates for these points (named "fake1" and "fake2") were determined by constructing an orthophoto mosaic from a preliminary version of the dense point cloud and extracting coordinates for the features from the mosaic. Vertical coordinates for these locations were determined from LiDAR data in the 2013-2014 U.S. Geological Survey CMGP LiDAR: Post Sandy (MA, NH, RI).
    6) The camera calibration was optimized using “Optimize Cameras”, using all of the lens-calibration coefficients except p3 and p4 and a tie-point accuracy of 1 pixel (set in “Reference Settings”). Refinement of the sparse point cloud The sparse point cloud representing tie points among the images consisted of approximately 1.5 million points. An iterative method developed by Tommy Noble (pers. communication, 2016) was used to identify and remove lower-quality tie points. This method involved using “Gradual Selection” of tie points, with the following criteria and target values.
    * Reconstruction uncertainty – Quality based on the geometry of the reconstruction. A dimensionless ratio of the maximum/minimum axes of the three-dimensional ellipse describing reconstruction uncertainty based on ray triangulation (target was 10)
    * Projection accuracy – Quality of pixel matching among images. A weighted ranking (1 is best, larger numbers worse) based on the size and sharpness of tie-points (target was 3)
    * Reprojection error – Estimate of residual error in tie-point location. A measure (pixels) of the precision of calculated tie-point locations based on the geometry (target was 0.3 pixels).
    “Gradual Selection” was used and the target value was set, but if more than about 20% of the points were flagged at that setting, the threshold was adjusted to select only about 10% of the points. (The total number of points and the number of flagged points was shown on screen as selections were made). Selected points were deleted, and camera settings were optimized before the next iteration. After each iteration, the improvement in accuracy was assessed by checking the marker error for ground control points and the standard unit weight error (SUWE). The SUWE is reported in the console pane and, ideally, should be close to 1 (dimensionless; Tommy Noble, pers. communication, 2016). This procedure was repeated three times for each criterion listed above (in order; i.e. points were selected based on reconstruction uncertainty three times before next selecting by projection accuracy). A final optimization was made after adjusting the tie-point accuracy to one-half pixel.
    At the end of this procedure, approximately one-third of the points had been removed, leaving 967,313 tie points, two photos were eliminated automatically because they had too few (less than about 200) tie points, and the following values for the target metrics were obtained: 
    * Reconstruction uncertainty - 10 (no units)
* Projection accuracy - 9 (no units)
* Reprojection error - 0.3 (pixels)
    The marker error for the ground control points was reduced to 0.021 m (0.188 pixels), and the SUWE increased from the initial value of 0.146 to 0.275.
    Finally, hand editing of the sparse point cloud was used to remove clearly erroneous points from that were offshore or significantly above or below ground level.
    Dense point cloud
    “Build Dense Cloud” was invoked with “High” quality and “Aggressive” depth filtering to generate a dense point cloud. "Export points" was used to export the point cloud in .LAZ format. The resulting dense point cloud containing 434,096,824 points is the data product distributed here.
    Estimate uncertainty of point cloud
    Uncertainty in the location of points in the dense point cloud is, in general, the quadrature sum of a) uncertainty in the locations of the ground control points (GCPs) to which the point cloud is referenced, plus b) uncertainty in the geometric reconstruction represented by the sparse point cloud, plus c) interpolation errors associated with placing the dense-cloud points in the geometric reconstruction. Uncertainty in the geometric reconstruction (b) includes uncertainty in the location of tie points, camera locations, camera look angles, and camera lens calibrations, assuming the GCP locations are exact. Interpolation errors (c) arise when the locations of dense-cloud points between sparse-cloud points differ from the real-world locations. The Photoscan software does not provide means to estimate (b) or (c), so we inferred that uncertainty from root-mean-square (RMS) errors in the reconstructed locations of ground control points, combined with the resolution of the images and the estimated reprojection error, all as reported by Photoscan. This is likely an underestimate of the uncertainty, because the reconstruction was optimized to match the ground control points.
    We estimated (a), the horizontal and vertical precision of the surveyed ground control point locations, as the RMS error for repeat measurements of survey reference marks taken at the beginning and end of the survey day, plus the reported error in the Online Positioning User Service (OPUS) solution for the primary reference point used to locate the survey. The combined horizontal and vertical uncertainties for surveyed locations of the GCPs were +/- 0.027 m and +/- 0.017 m, respectively. Our replacements for (b) were the horizontal and vertical RMS errors associated with reconstruction of the GCP marker locations reported by Photoscan (+/- 0.019 m and +/- 0.009 m, respectively). In lieu of values for (c), we combined the reported unscaled reprojection error reported by Photoscan (0.3 pixels) with the resolution of the images (1 pixel equaled approximately 0.04 m in nadir views) to derive a reprojection error of 0.012 m.
    We combined all of these uncertainties to establish minimum estimates of the horizontal and vertical uncertainties in real-world coordinates of the reconstructed points as +/- 0.035 and +/- 0.022 m, respectively.
    One check on these values emerges from comparisons of 144 field measurements against the digital elevation model (DEM) generated from the point cloud. The field measurements are provided as transect points in Sherwood (2016; the DEM is not provided in this release). The independent elevation measurements were made with survey-grade GPS, and were compared with values extracted from the closest point in the DEM. The mean vertical difference was 0.001 m, the RMS difference was 0.06 m, and the minimum and maximum differences were -0.23 m and 0.16 m. Person who carried out this activity:
    U. S. Geological Survey, Woods Hole Coastal and Marine Science Center
    Attn: Christopher R. Sherwood
    Research Oceanographer
    384 Woods Hole Road
    Woods Hole, Massachusetts
    U.S.A.

    508 457 2269 (voice)
    508 457 2310 (FAX)
    csherwood@usgs.gov
    Date: 20-Jul-2018 (process 2 of 3)
    USGS Thesaurus keywords added to the keyword section. Person who carried out this activity:
    U.S. Geological Survey
    Attn: VeeAnn A. Cross
    Marine Geologist
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 x2251 (voice)
    508-457-2310 (FAX)
    vatnipp@usgs.gov
    Date: 07-Aug-2020 (process 3 of 3)
    Added keywords section with USGS persistent identifier as theme keyword. Person who carried out this activity:
    U.S. Geological Survey
    Attn: VeeAnn A. Cross
    Marine Geologist
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 x2251 (voice)
    508-457-2310 (FAX)
    vatnipp@usgs.gov
  3. What similar or related data should the user be aware of?
    Isenburg, Martin, 2013, LASzip: Lossless compression of LiDAR data: Phtogrammetric Engineering & Remote Sensing Vol. 79, No. 2, February 2013, pp. 209-217, American Society for Photogrammetry & Remote Sensing, Bethesda, MD.

    American Society for Photogrammetry & Remote Sensing (ASPRS), 2013, LAS SPECIFICATION VERSION 1.4 – R13: American Society for Photogrammetry & Remote Sensing, Bethesda, MD.

    Online Links:

    Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM), United States Geological Survey (USGS), Coastal and Marine Geology Program (CMGP), and Woolpert, 20150615, 2013-2014 U.S. Geological Survey CMGP LiDAR: Post Sandy (MA, NH, RI): NOAA's Ocean Service, Office for Coastal Management (OCM), Charleston, SC.

    Online Links:

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
  2. How accurate are the geographic locations?
    Horizontal positions of individual points were calculated by photogrammetric software and ground control points. We cannot evaluate the accuracy of individual points, but digital elevation models based on the point cloud differ from independently measured points with an absolute horizontal error of less than 10 cm and an RMS vertical error of approximately 6 cm. However, biases in some portions of the map area may cause greater errors (up to 50 cm horizontal and 1 m vertical), and some individual points may have gross errors. The horizontal coordinate system is in NAD83(2011) UTM Zone 19 North (meters).
  3. How accurate are the heights or depths?
    Vertical positions of individual points were calculated using photogrammetric software and ground control points. We cannot evaluate the accuracy of individual points, but digital elevation models based on the point cloud differ from independently measured points with an absolute horizontal error of less than 10 cm and an RMS vertical error of approximately 6 cm. However, biases in some portions of the map area may cause greater errors (up to 50 cm horizontal and 1 m vertical), and some individual points may have gross errors. The vertical coordinate system is NAVD88 (meters).
  4. Where are the gaps in the data? What is missing?
    The point cloud was constructed from 1122 of the 1246 images obtained during flight 1 from camera rgb2. Some images were eliminated during photogrammetric processing because they contained insufficient numbers of reliable tie points relating them to ground features visible in adjacent images. Many of these were images containing views of mostly water or oblique views that included the horizon. All of the points generated by the software to form a dense cloud have been included in the data release. That includes points that likely do not represent ground features, but are instead artifacts generated by moving water surfaces and or erroneous tie points. These points are commonly eliminated through either automatic or manual classification, and have been retained to allow experimentation with point classification methods.
  5. How consistent are the relationships among the observations, including topology?
    The point cloud was constructed using photogrammetry software (Agisoft Photoscan Professional v. 1.2.6) and 1122 digital images taken approximately 120 m above the ground on 1 March, 2016 with a Canon Powershot SX280 12-mexapixel digital camera mounted in a Skywalker X8 flying wing operated by Raptor Maps, Inc., contractors to the U.S. Geological Survey. The images were collected during flight 1 using the camera designated rgb2 and have been published by Sherwood (2016), see source citation. Some of the images were eliminated during photogrammetric processing, but all of the points generated in the dense point cloud are included in this data release. That includes points that likely do not represent ground features, but are instead artifacts generated by moving water surfaces and erroneous tie points.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints none
Use_Constraints Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey as the originator of the dataset.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? There is one LAZ file containing the points (caco_f1_rgb2_v10_points.laz). A browse graphic (CACO_2016-03-01_point_cloud_browse_image.jpg) and the associated CSDGM FGDC metadata in XML format is also available for download.
  3. What legal disclaimers am I supposed to read?
    Neither the U.S. Government, the Department of the Interior, nor the USGS, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of these data or related materials. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  4. How can I download or order the data?
  5. What hardware or software do I need in order to use the data set?
    These files require software capable of opening binary LAZ files.

Who wrote the metadata?

Dates:
Last modified: 19-Mar-2024
Metadata author:
U.S. Geological Survey
Attn: Christopher R. Sherwood
Research Oceanographer
384 Woods Hole Road
Woods Hole, Massachusetts

508-548-8700 x2269 (voice)
508-457-2310 (FAX)
whsc_data_contact@usgs.gov
Contact_Instructions:
The metadata contact email address is a generic address in the event the person is no longer with USGS. (updated on 20240319)
Metadata standard:
FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/SB_data_release/DR_F75M63WJ/CACO_UAS_Point_Cloud.faq.html>
Generated by mp version 2.9.51 on Wed Jun 26 15:25:01 2024